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On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding. (arXiv:2210.05291v1 [cs.CL])
Oct. 12, 2022, 1:17 a.m. | Gaëlle Laperrière, Valentin Pelloin, Mickaël Rouvier, Themos Stafylakis, Yannick Estève
cs.CL updates on arXiv.org arxiv.org
In this paper we examine the use of semantically-aligned speech
representations for end-to-end spoken language understanding (SLU). We employ
the recently-introduced SAMU-XLSR model, which is designed to generate a single
embedding that captures the semantics at the utterance level, semantically
aligned across different languages. This model combines the acoustic
frame-level speech representation learning model (XLS-R) with the Language
Agnostic BERT Sentence Embedding (LaBSE) model. We show that the use of the
SAMU-XLSR model instead of the initial XLS-R model improves …
arxiv language speech spoken language understanding understanding
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